Many applications utilizing digital image signals employ a hierarchical decomposition scheme. An article by Burt and Adelson entitled "The Laplacian Pyramid as a Compact Image Code, "IEEE Trans. Commun., COM-31, 1983, p.p. 532-540 describes an encoding method wherein the original digital image signal is low pass filtered, and this low pass filtered digital image signal is decimated (subsampled) to take advantage of its reduced bandwidth. The decomposition process of low pass filtering and decimating is then repeated in successive steps to form a hierarchical structure or pyramid. The low spatial resolution digital image signals resulting from the low pass filtering and decimating steps, are interpolated and high spatial resolution residual digital image signals are formed to account for the incomplete reconstruction of a higher spatial resolution digital image signals from interpolated low spatial resolution digital image signals. Digital image signal data compression advantage can be realized as these residual digital image signals have reduced variance and may be more aggressively quantized.
As mentioned above, a hierarchical scheme might use a low spatial resolution digital image signal that is interpolated to form a high spatial resolution digital image signal and added to a high spatial resolution residual digital image signal that contains the high spatial resolution data not produced in the process of interpolating the low resolution digital image signal. Alternatively, subband or other decomposition schemes, and or combinations of schemes (such as using subbands for a middle resolution digital image signal and a residual for the highest resolution digital image signal) could also be implemented in a hierarchical manner.
Given limited resources for storing, transmitting, processing, or otherwise manipulating a digital image signal, it is desirable to compress the data load or size of these digital image signals. There are well known techniques for achieving image data compression that can include applying transformation techniques and/or quantizing (reducing the number of dynamic range levels) prior to coding the digital image signal for data compression. These techniques to achieve digital image signal data compression may be applied to any or all of the digital image signals resulting from a hierarchical decomposition. Typically, the higher spatial resolution components, whose higher spatial resolutions yield higher data loads, are aggressively compressed by quantizing and applying lossy compression techniques.
These techniques can also be applied to a multiuse environment. A multiuse hierarchical decomposition and reconstruction scheme permits fast access to low spatial resolution representations of these digital image signals. In addition, a multiuse featured hierarchical scheme then allows higher spatial resolution representations of these digital image signals to be reconstructed from these low spatial resolution digital image signals by appropriate image processing which includes adding, through the appropriate algorithm, the information necessary to achieve these higher spatial resolution digital image signals.